The complexity of modern wireless networks exceeds the ability of users and operators to optimally configure them. Autonomic capability is much of a necessity than desire and a viable solution to incorporate greater degrees of intelligence within the network itself. Exploitation of the Cognitive networking paradigm is an approach towards imparting autonomic capabilities to network elements. Cognitive networks are formed when a collection of communication nodes organize to achieve network-level goals with the aid of some form of local cognitive processing. It will involve important aspects such as radio resource management, network management, services delivery and secure configuration mechanisms.
One of the ways to improve the performance of a generic optimum solution search process is to incorporate an awareness of problem semantics into the algorithm. A typical optimization problem consists of the maximization or minimization of an objective function, expressed in terms of a set of variables with their respective domains, subject to a set of constraints. But, this does not include any domain specific concepts or facts. Use of domain ontology in the solution process could make it more efficient.
In general, optimization problems involved in wireless networks which are nonlinear. Many of the cause-effect relations involved are analytically in-tractable. Many of the traditional non-linear optimization algorithms are not suitable for on-line optimization due to their computational complexity and the requirement of a system model. Alternately algorithms following a black box approach such as genetic algorithms, simulated annealing, etc could be explored. However, they do not assure global optimality and do not use problem semantics to improve the solution search process.
Cognitive engine following a cognitive cycle has been proposed in literature for intelligent management of wireless communication links and networks. The cognitive engine is a computational module (software/hardware) that may sit at a terminal, base station or other network elements. The cognitive cycle involves steps such as sensing, analyzing, planning and acting along with learning in a dynamic environment. This engine supports autonomic behavior of the network such as self-configuration, self-optimization, self-healing etc.
Creating a dynamic model of the system is essential for imparting intelligent adaptive control of the system. This is generally done through analytic models, computational models, probabilistic models, rules etc. A suitable learning mechanism needs to be there to update the knowledge base.
For complex dynamic systems such as wireless networks, a complete and accurate model of the system is difficult and analytically in-tractable. A high resolution model that is not accurate may give good performance in certain situations but severely degraded in other cases. On the contrary a low resolution model that is accurate performance but more robust. Machine learning techniques can be used to improve the quality and accuracy of the model.
An online control approach directly experiments the system for a better configuration in the absence of a guiding model. In this approach there is a danger that the system may get into highly degraded performance levels or unacceptable operating points. However if there is a learning mechanism incorporated, the system can avoid such bad operating points/configurations in future. Further the use of a knowledge base that qualitatively model the system behavior can help in reducing the number of iterations.
An online control approach supported with a low resolution model and a learning mechanism would be a good option for cognitive engine for the intelligent and robust control of dynamic systems where high resolution accurate models are infeasible.
One or the other prior art provides a radio network management system which comprises at least one centralized node. The centralized node includes a radio transceiver having more than one adjustable parameter. The centralized node also includes at least one adaptive tuning engine configured to make changes to the at least one adjustable parameter. A weighted analysis function is configured to provide a weighted analysis based on the output of the at least one adaptive tuning engine. Further, a cognitive learning function is configured to provide feedback to make optimally directed adjustments to the at least one adaptive tuning engine.
Also, genetic algorithm (GA) approach is used to adapt a wireless radio to a changing environment. A cognitive radio engine implements three algorithms; a wireless channel genetic algorithm (WCGA), a cognitive system monitor (CSM) and a wireless system genetic algorithm (WSGA). A chaotic search with controllable boundaries allows the cognitive radio engine to seek out and discover unique solutions efficiently.
Some of the lacunae that exists in the prior art discussed above are that, firstly they discloses systems and methods that are not suitable for on-line optimization due to their computational complexity and the requirement of a system model. Some others treat the system as a black box and convergence of such approaches are slow with an element of uncertainty. There is a possibility of a grey box approach which is a middle path that exploits knowledge from an expert to guide through the optimization process more efficiently.
Therefore, there is a need of a system and method which is capable of providing a near optimum configuration for wireless networks by exploiting the qualitative knowledge of an expert on the system at a low complexity. The system and method should be capable of enabling reconfiguration of the network by controlling the network parameters while reducing the complexity.